Dietary specialists consume specific prey items,and they are often morphologically and behaviorally specialized to feed efficiently on those prey animals.Among specialist snakes,consumption of terrestrial arthropods i...Dietary specialists consume specific prey items,and they are often morphologically and behaviorally specialized to feed efficiently on those prey animals.Among specialist snakes,consumption of terrestrial arthropods is relatively rare.Because most terrestrial arthropods possess hardened sclerites and appendages,it is possible that snakes that feed on arthropods would show specialized prey-handling behavior.In this study,we describe prey-handling behavior of a snake feeding on terrestrial arthropods,which hitherto has not been well documented.We focused on Rhabdophis chiwen,which mainly feeds on earthworms,but also consumes lampyrine firefly larvae,sequestering cardiotonic steroids from them in its defensive organs,called nucho-dorsal glands.When feeding on earthworms,snakes showed size-dependent selection of swallowing direction,but this tendency was not observed when feeding on firefly larvae.Manipulation of firefly larvae did not seem to be efficient,probably because they possess sclerites and appendages such as legs that impede smooth handling.Although fireflies are an essential food for R.chiwen as a toxin source,our results showed that the snake is not adept at handling firefly larvae compared to earthworms,implying that dietary specialization does not necessarily accompany behavioral specialization.We discuss possible reasons for this inconsistency.展开更多
The Internet of Things(IoT)has emerged as an important future technology.IoT-Fog is a new computing paradigm that processes IoT data on servers close to the source of the data.In IoT-Fog computing,resource allocation ...The Internet of Things(IoT)has emerged as an important future technology.IoT-Fog is a new computing paradigm that processes IoT data on servers close to the source of the data.In IoT-Fog computing,resource allocation and independent task scheduling aim to deliver short response time services demanded by the IoT devices and performed by fog servers.The heterogeneity of the IoT-Fog resources and the huge amount of data that needs to be processed by the IoT-Fog tasks make scheduling fog computing tasks a challenging problem.This study proposes an Adaptive Firefly Algorithm(AFA)for dependent task scheduling in IoT-Fog computing.The proposed AFA is a modified version of the standard Firefly Algorithm(FA),considering the execution times of the submitted tasks,the impact of synchronization requirements,and the communication time between dependent tasks.As IoT-Fog computing depends mainly on distributed fog node servers that receive tasks in a dynamic manner,tackling the communications and synchronization issues between dependent tasks is becoming a challenging problem.The proposed AFA aims to address the dynamic nature of IoT-Fog computing environments.The proposed AFA mechanism considers a dynamic light absorption coefficient to control the decrease in attractiveness over iterations.The proposed AFA mechanism performance was benchmarked against the standard Firefly Algorithm(FA),Puma Optimizer(PO),Genetic Algorithm(GA),and Ant Colony Optimization(ACO)through simulations under light,typical,and heavy workload scenarios.In heavy workloads,the proposed AFA mechanism obtained the shortest average execution time,968.98 ms compared to 970.96,1352.87,1247.28,and 1773.62 of FA,PO,GA,and ACO,respectively.The simulation results demonstrate the proposed AFA’s ability to rapidly converge to optimal solutions,emphasizing its adaptability and efficiency in typical and heavy workloads.展开更多
This research introduces a novel approach to enhancing bucket elevator design and operation through the integration of discrete element method(DEM)simulation,design of experiments(DOE),and metaheuristic optimization a...This research introduces a novel approach to enhancing bucket elevator design and operation through the integration of discrete element method(DEM)simulation,design of experiments(DOE),and metaheuristic optimization algorithms.Specifically,the study employs the firefly algorithm(FA),a metaheuristic optimization technique,to optimize bucket elevator parameters for maximizing transport mass and mass flow rate discharge of granular materials under specified working conditions.The experimental methodology involves several key steps:screening experiments to identify significant factors affecting bucket elevator operation,central composite design(CCD)experiments to further explore these factors,and response surface methodology(RSM)to create predictive models for transport mass and mass flow rate discharge.The FA algorithm is then applied to optimize these models,and the results are validated through simulation and empirical experiments.The study validates the optimized parameters through simulation and empirical experiments,comparing results with DEM simulation.The outcomes demonstrate the effectiveness of the FA algorithm in identifying optimal bucket parameters,showcasing less than 10%and 15%deviation for transport mass and mass flow rate discharge,respectively,between predicted and actual values.Overall,this research provides insights into the critical factors influencing bucket elevator operation and offers a systematic methodology for optimizing bucket parameters,contributing to more efficient material handling in various industrial applications.展开更多
To segment defects from the quad flat non-lead QFN package surface a multilevel Otsu thresholding method based on the firefly algorithm with opposition-learning is proposed. First the Otsu thresholding algorithm is ex...To segment defects from the quad flat non-lead QFN package surface a multilevel Otsu thresholding method based on the firefly algorithm with opposition-learning is proposed. First the Otsu thresholding algorithm is expanded to a multilevel Otsu thresholding algorithm. Secondly a firefly algorithm with opposition-learning OFA is proposed.In the OFA opposite fireflies are generated to increase the diversity of the fireflies and improve the global search ability. Thirdly the OFA is applied to searching multilevel thresholds for image segmentation. Finally the proposed method is implemented to segment the QFN images with defects and the results are compared with three methods i.e. the exhaustive search method the multilevel Otsu thresholding method based on particle swarm optimization and the multilevel Otsu thresholding method based on the firefly algorithm. Experimental results show that the proposed method can segment QFN surface defects images more efficiently and at a greater speed than that of the other three methods.展开更多
Diaphanes is the fourth largest genus in Lampyridae, but no luciferase gene from this genus has been reported. In this paper, by PCR amplification of the genomic DNA, the luciferase gene of Diaphanes pectinealis, whic...Diaphanes is the fourth largest genus in Lampyridae, but no luciferase gene from this genus has been reported. In this paper, by PCR amplification of the genomic DNA, the luciferase gene of Diaphanes pectinealis, which is the first case from Diaphanes, was identified and sequenced. The luciferase gene from D. pectinealis spans 1958 base pairs （bp） from the start to the stop codon, including seven exons separated by six introns, and encoding a 547-residuelong polypeptide. Its deduced amino acid sequence showed high protein similarity to those of the Lampyrini tribe （93 - 94% ） and the Cratomorphini tribe （92%）, while low similarity was found with the North American firefly Photinus pyralis （83%） of the Photinini tribe within the same subfamily Lampyrinae. The phylogenetic analysis performed with the deduced amino acid sequences of the luciferase gene further confirms that D. pectinealis, Pyrocoelia, Lampyris, Cratomorphus, and Photinus belong to the same subfamily Lampyrinae, and Diaphanes is closely related to Pyrocoelia, Lampyris, and Cratomorphus. Furthemore, the phylogenetic analysis based on the nucleotide sequences of the luciferase gene indicates Diaphanes is a sister to Lampyris. The phylogenetic analyses are partly consistent with morphological （Branham ＆ Wenzel, 2003） and mitochondrial DNA analyses （Li et al, 2006）.展开更多
Rayleigh waves have high amplitude, low frequency, and low velocity, which are treated as strong noise to be attenuated in reflected seismic surveys. This study addresses how to identify useful shear wave velocity pro...Rayleigh waves have high amplitude, low frequency, and low velocity, which are treated as strong noise to be attenuated in reflected seismic surveys. This study addresses how to identify useful shear wave velocity profile and stratigraphic information from Rayleigh waves. We choose the Firefly algorithm for inversion of surface waves. The Firefly algorithm, a new type of particle swarm optimization, has the advantages of being robust, highly effective, and allows global searching. This algorithm is feasible and has advantages for use in Rayleigh wave inversion with both synthetic models and field data. The results show that the Firefly algorithm, which is a robust and practical method, can achieve nonlinear inversion of surface waves with high resolution.展开更多
The cDNA encoding the luciferase from lantern mRNA of one diurnal firefly Pyrocoelia pygidialis Pic, 1926 has been cloned, sequenced and functionally expressed. The cDNA sequence of P pygidialis luciferase is 1647 bas...The cDNA encoding the luciferase from lantern mRNA of one diurnal firefly Pyrocoelia pygidialis Pic, 1926 has been cloned, sequenced and functionally expressed. The cDNA sequence of P pygidialis luciferase is 1647 base pairs in length, coding a protein of 548 amino acid residues. Sequence analysis of the deduced amino acid sequence showed that this luciferase had 97.8% resemblance to luciferases from the fireflies Lampyris noctiluca, Lampyris turkestanicus and Nyctophila cf. caucasica. Phylogenetic analysis using deduced amino acid sequence showed that P pygidialis located at the base of Lampyris＋Nyctophila clade with robust support （BP=97%）; but did not show a monophyletic relationship with its congeneric species P pectoralis, P tufa and P miyako, all three are strong luminous and nocturnal species. The expression worked in recombinant Escherichia coli. Expression product had a 70kDa band and emitted yellow-green luminescence in the presence of luciferin. Five loops in the P pygidialis luciferase, L1 （NI98-G208）, L2 （T240-G247）, L3 （G317-K322）, L4 （L343-I350） and L5 （G522-D532）, were found from the structure modeling analysis in the cleft, where it was considered the active site for the substrate compound entering and binding. Different amino acid residues between the luciferases of P. pygidialis and the three other known strong luminous species can not explain the situation of weak or strong luminescence. Future study of these loops, residues or crystal structure analysis may be helpful in understanding the real differences between the luciferases between diurnal and nocturnal species.展开更多
The Firefly Algorithm(FA)is a highly efficient population-based optimization technique developed by mimicking the flashing behavior of fireflies when mating.This article proposes a method based on Differential Evoluti...The Firefly Algorithm(FA)is a highly efficient population-based optimization technique developed by mimicking the flashing behavior of fireflies when mating.This article proposes a method based on Differential Evolution(DE)/current-to-best/1 for enhancing the FA's movement process.The proposed modification increases the global search ability and the convergence rates while maintaining a balance between exploration and exploitation by deploying the global best solution.However,employing the best solution can lead to premature algorithm convergence,but this study handles this issue using a loop adjacent to the algorithm's main loop.Additionally,the suggested algorithm’s sensitivity to the alpha parameter is reduced compared to the original FA.The GbFA surpasses both the original and five-version of enhanced FAs in finding the optimal solution to 30 CEC2014 real parameter benchmark problems with all selected alpha values.Additionally,the CEC 2017 benchmark functions and the eight engineering optimization challenges are also utilized to evaluate GbFA’s efficacy and robustness on real-world problems against several enhanced algorithms.In all cases,GbFA provides the optimal result compared to other methods.Note that the source code of the GbFA algorithm is publicly available at https://www.optim-app.com/projects/gbfa.展开更多
Classifying the data into a meaningful group is one of the fundamental ways of understanding and learning the valuable information. High-quality clustering methods are necessary for the valuable and efficient analysis...Classifying the data into a meaningful group is one of the fundamental ways of understanding and learning the valuable information. High-quality clustering methods are necessary for the valuable and efficient analysis of the increasing data. The Firefly Algorithm (FA) is one of the bio-inspired algorithms and it is recently used to solve the clustering problems. In this paper, Hybrid F-Firefly algorithm is developed by combining the Fuzzy C-Means (FCM) with FA to improve the clustering accuracy with global optimum solution. The Hybrid F-Firefly algorithm is developed by incorporating FCM operator at the end of each iteration in FA algorithm. This proposed algorithm is designed to utilize the goodness of existing algorithm and to enhance the original FA algorithm by solving the shortcomings in the FCM algorithm like the trapping in local optima and sensitive to initial seed points. In this research work, the Hybrid F-Firefly algorithm is implemented and experimentally tested for various performance measures under six different benchmark datasets. From the experimental results, it is observed that the Hybrid F-Firefly algorithm significantly improves the intra-cluster distance when compared with the existing algorithms like K-means, FCM and FA algorithm.展开更多
Autonomous mobile robot navigation is one of the most emerging areas of research by using swarm intelligence. Path planning and obstacle avoidance are most researched current topics like navigational challenges for mo...Autonomous mobile robot navigation is one of the most emerging areas of research by using swarm intelligence. Path planning and obstacle avoidance are most researched current topics like navigational challenges for mobile robot. The paper presents application and implementation of Firefly Algorithm(FA)for Mobile Robot Navigation(MRN) in uncertain environment. The uncertainty is defined over the changing environmental condition from static to dynamic. The attraction of one firefly towards the other firefly due to variation of their brightness is the key concept of the proposed study. The proposed controller efficiently explores the environment and improves the global search in less number of iterations and hence it can be easily implemented for real time obstacle avoidance especially for dynamic environment. It solves the challenges of navigation, minimizes the computational calculations, and avoids random moving of fireflies. The performance of proposed controller is better in terms of path optimality when compared to other intelligent navigational approaches.展开更多
Essentially, it is significant to supply the consumer with reliable and sufficient power. Since, power quality is measured by the consistency in frequency and power flow between control areas. Thus, in a power system ...Essentially, it is significant to supply the consumer with reliable and sufficient power. Since, power quality is measured by the consistency in frequency and power flow between control areas. Thus, in a power system operation and control,automatic generation control(AGC) plays a crucial role. In this paper, multi-area(Five areas: area 1, area 2, area 3, area 4 and area 5) reheat thermal power systems are considered with proportional-integral-derivative(PID) controller as a supplementary controller. Each area in the investigated power system is equipped with appropriate governor unit, turbine with reheater unit, generator and speed regulator unit. The PID controller parameters are optimized by considering nature bio-inspired firefly algorithm(FFA). The experimental results demonstrated the comparison of the proposed system performance(FFA-PID)with optimized PID controller based genetic algorithm(GAPID) and particle swarm optimization(PSO) technique(PSOPID) for the same investigated power system. The results proved the efficiency of employing the integral time absolute error(ITAE) cost function with one percent step load perturbation(1 % SLP) in area 1. The proposed system based FFA achieved the least settling time compared to using the GA or the PSO algorithms, while, it attained good results with respect to the peak overshoot/undershoot. In addition, the FFA performance is improved with the increased number of iterations which outperformed the other optimization algorithms based controller.展开更多
Wireless Sensor Networks(WSNs)comprises low power devices that are randomly distributed in a geographically isolated region.The energy consumption of nodes is an essential factor to be considered.Therefore,an improved...Wireless Sensor Networks(WSNs)comprises low power devices that are randomly distributed in a geographically isolated region.The energy consumption of nodes is an essential factor to be considered.Therefore,an improved energy management technique is designed in this investigation to reduce its consumption and to enhance the network’s lifetime.This can be attained by balancing energy clusters using a meta-heuristic Firefly algorithm model for network communication.This improved technique is based on the cluster head selection technique with measurement of the tour length of fireflies.Time Division Multiple Access(TDMA)scheduler is also improved with the characteristics/behavior of fireflies and also executed.At last,the development approach shows the progression of the network lifetime,the total number of selected Cluster Heads(CH),the energy consumed by nodes,and the number of packets transmitted.This approach is compared with Ad hoc On-Demand Distance Vector(AODV),Dynamic Source Routing(DSR)and Low Energy Adaptive Clustering Hierarchy(LEAH)protocols.Simulation is performed in MATLAB with the numerical outcomes showing the efficiency of the proposed approach.The energy consumption of sensor nodes is reduced by about 50%and increases the lifetime of nodes by 78%more than AODV,DSR and LEACH protocols.The parameters such as cluster formation,end to end delay,percentage of nodes alive and packet delivery ratio,are also evaluated...The anticipated method shows better trade-off in contrast to existing techniques.展开更多
This paper introduces a new approach of firefly algorithm based on opposition-based learning （OBFA） to enhance the global search ability of the original algorithm. The new algorithm employs opposition based learning...This paper introduces a new approach of firefly algorithm based on opposition-based learning （OBFA） to enhance the global search ability of the original algorithm. The new algorithm employs opposition based learning concept to generate initial population and also updating agents’ positions. The proposed OBFA is applied for minimization of the factor of safety and search for critical failure surface in slope stability analysis. The numerical experiments demonstrate the effectiveness and robustness of the new algorithm.展开更多
Firefly algorithm(FA)is a recently-proposed swarm intelligence technique.It has shown good performance in solving various optimization problems.According to the standard firefly algorithm and most of its variants,a fi...Firefly algorithm(FA)is a recently-proposed swarm intelligence technique.It has shown good performance in solving various optimization problems.According to the standard firefly algorithm and most of its variants,a firefly migrates to every other brighter firefly in each iteration.However,this method leads to defects of oscillations of positions,which hampers the convergence to the optimum.To address these problems and enhance the performance of FA,we propose a new firefly algorithm,which is called the Best Neighbor Firefly Algorithm(BNFA).It employs the best neighbor guided strategy,where each firefly is attracted to the best firefly among some randomly chosen neighbors,thus reducing the firefly oscillations in every attraction-induced migration stage,while increasing the probability of the guidance a new better direction.Moreover,it selects neighbors randomly to prevent the firefly form being trapped into a local optimum.Extensive experiments are conducted to find out the optimal parameter settings.To verify the performance of BNFA,13 classical benchmark functions are tested.Results show that BNFA outperforms the standard FA and other recently proposed modified FAs.展开更多
Given a connected undirected graph G whose edges are labeled,the minimumlabeling spanning tree(MLST)problemis to find a spanning tree of G with the smallest number of different labels.TheMLST is anNP-hard combinatoria...Given a connected undirected graph G whose edges are labeled,the minimumlabeling spanning tree(MLST)problemis to find a spanning tree of G with the smallest number of different labels.TheMLST is anNP-hard combinatorial optimization problem,which is widely applied in communication networks,multimodal transportation networks,and data compression.Some approximation algorithms and heuristics algorithms have been proposed for the problem.Firefly algorithm is a new meta-heuristic algorithm.Because of its simplicity and easy implementation,it has been successfully applied in various fields.However,the basic firefly algorithm is not suitable for discrete problems.To this end,a novel discrete firefly algorithm for the MLST problem is proposed in this paper.A binary operation method to update firefly positions and a local feasible handling method are introduced,which correct unfeasible solutions,eliminate redundant labels,and make the algorithm more suitable for discrete problems.Computational results show that the algorithm has good performance.The algorithm can be extended to solve other discrete optimization problems.展开更多
In this paper, a new algorithm which integrates the powerful firefly Mgorithm （FA） and the ant colony optimization （ACO） has been used in tracking control of ship steering for optimization of fractional-order prop...In this paper, a new algorithm which integrates the powerful firefly Mgorithm （FA） and the ant colony optimization （ACO） has been used in tracking control of ship steering for optimization of fractional-order proportional-integral-derivative （FOPID） controller gains. Particle swarm optimization （PSO） algorithm is also used to optimize FOPID controllers, and their performances are compared. It is found that FA optimized FOPID controller gives better performance than others. Sensitivity analysis has been carried out to see the robustness of optimum FOPID gains obtained at nominal conditions to wide changes in system parameters, and the optimum FOPID gains need not be reset for wide changes in system parameters.展开更多
To achieve optimal configuration of switching devices in a power distribution system,this paper proposes a repulsive firefly algorithm-based optimal switching device placement method.In this method,the influence of te...To achieve optimal configuration of switching devices in a power distribution system,this paper proposes a repulsive firefly algorithm-based optimal switching device placement method.In this method,the influence of territorial repulsion during firefly courtship is considered.The algorithm is practically applied to optimize the position and quantity of switching devices,while avoiding its convergence to the local optimal solution.The experimental simulation results have showed that the proposed repulsive firefly algorithm is feasible and effective,with satisfying global search capability and convergence speed,holding potential applications in setting value calculation of relay protection and distribution network automation control.展开更多
Software development effort estimation is considered a fundamental task for software development life cycle as well as for managing project cost, time and quality. Therefore, accurate estimation is a substantial facto...Software development effort estimation is considered a fundamental task for software development life cycle as well as for managing project cost, time and quality. Therefore, accurate estimation is a substantial factor in projects success and reducing the risks. In recent years, software effort estimation has received a considerable amount of attention from researchers?and became a challenge for software industry. In the last two decades, many researchers and practitioners proposed statistical and machine learning-based models for software effort estimation. In this work, Firefly Algorithm is proposed as a metaheuristic optimization method for optimizing the parameters of three COCOMO-based models. These models include the basic COCOMO model and other two models proposed in the literature as extensions of the basic COCOMO model. The developed estimation models are evaluated using different evaluation metrics. Experimental results show high accuracy and significant error minimization of Firefly Algorithm over other metaheuristic optimization algorithms including Genetic Algorithms and Particle Swarm Optimization.展开更多
Congestion in Wireless Sensor Network (WSN) is an issue of concern for several researchers in recent years. The key challenge is to develop an algorithmic rule which may realize the optimased route on the idea of para...Congestion in Wireless Sensor Network (WSN) is an issue of concern for several researchers in recent years. The key challenge is to develop an algorithmic rule which may realize the optimased route on the idea of parameters like residual energy, range of retransmissions and the distance between source and destination. The Firefly Algorithmic rule is implemented in this paper that relies on the attractiveness issue of the firefly insect to control congestion in WSN at transport layer. The results additionally show that the projected approach is best as compared to Congestion Detection and Avoidance (CODA) and Particle Swarm Optimization (PSO) on network lifetime and throughput of the network.展开更多
基金supported in part by JST SPRING(JPMJSP2110)by Japan Society for the Promotion of Science Grants-in-Aid for Scientific Research(18KK0205,21H02551)Shosuke Takeuchi for personal funding.
文摘Dietary specialists consume specific prey items,and they are often morphologically and behaviorally specialized to feed efficiently on those prey animals.Among specialist snakes,consumption of terrestrial arthropods is relatively rare.Because most terrestrial arthropods possess hardened sclerites and appendages,it is possible that snakes that feed on arthropods would show specialized prey-handling behavior.In this study,we describe prey-handling behavior of a snake feeding on terrestrial arthropods,which hitherto has not been well documented.We focused on Rhabdophis chiwen,which mainly feeds on earthworms,but also consumes lampyrine firefly larvae,sequestering cardiotonic steroids from them in its defensive organs,called nucho-dorsal glands.When feeding on earthworms,snakes showed size-dependent selection of swallowing direction,but this tendency was not observed when feeding on firefly larvae.Manipulation of firefly larvae did not seem to be efficient,probably because they possess sclerites and appendages such as legs that impede smooth handling.Although fireflies are an essential food for R.chiwen as a toxin source,our results showed that the snake is not adept at handling firefly larvae compared to earthworms,implying that dietary specialization does not necessarily accompany behavioral specialization.We discuss possible reasons for this inconsistency.
基金the Deanship of Graduate Studies and Scientific Research at Najran University for funding this work under the Easy Funding Program grant code(NU/EFP/SERC/13/166).
文摘The Internet of Things(IoT)has emerged as an important future technology.IoT-Fog is a new computing paradigm that processes IoT data on servers close to the source of the data.In IoT-Fog computing,resource allocation and independent task scheduling aim to deliver short response time services demanded by the IoT devices and performed by fog servers.The heterogeneity of the IoT-Fog resources and the huge amount of data that needs to be processed by the IoT-Fog tasks make scheduling fog computing tasks a challenging problem.This study proposes an Adaptive Firefly Algorithm(AFA)for dependent task scheduling in IoT-Fog computing.The proposed AFA is a modified version of the standard Firefly Algorithm(FA),considering the execution times of the submitted tasks,the impact of synchronization requirements,and the communication time between dependent tasks.As IoT-Fog computing depends mainly on distributed fog node servers that receive tasks in a dynamic manner,tackling the communications and synchronization issues between dependent tasks is becoming a challenging problem.The proposed AFA aims to address the dynamic nature of IoT-Fog computing environments.The proposed AFA mechanism considers a dynamic light absorption coefficient to control the decrease in attractiveness over iterations.The proposed AFA mechanism performance was benchmarked against the standard Firefly Algorithm(FA),Puma Optimizer(PO),Genetic Algorithm(GA),and Ant Colony Optimization(ACO)through simulations under light,typical,and heavy workload scenarios.In heavy workloads,the proposed AFA mechanism obtained the shortest average execution time,968.98 ms compared to 970.96,1352.87,1247.28,and 1773.62 of FA,PO,GA,and ACO,respectively.The simulation results demonstrate the proposed AFA’s ability to rapidly converge to optimal solutions,emphasizing its adaptability and efficiency in typical and heavy workloads.
基金This research was funded by the Faculty of Engineering,King Mongkut’s University of Technology North Bangkok.Contract No.ENG-NEW-66-39.
文摘This research introduces a novel approach to enhancing bucket elevator design and operation through the integration of discrete element method(DEM)simulation,design of experiments(DOE),and metaheuristic optimization algorithms.Specifically,the study employs the firefly algorithm(FA),a metaheuristic optimization technique,to optimize bucket elevator parameters for maximizing transport mass and mass flow rate discharge of granular materials under specified working conditions.The experimental methodology involves several key steps:screening experiments to identify significant factors affecting bucket elevator operation,central composite design(CCD)experiments to further explore these factors,and response surface methodology(RSM)to create predictive models for transport mass and mass flow rate discharge.The FA algorithm is then applied to optimize these models,and the results are validated through simulation and empirical experiments.The study validates the optimized parameters through simulation and empirical experiments,comparing results with DEM simulation.The outcomes demonstrate the effectiveness of the FA algorithm in identifying optimal bucket parameters,showcasing less than 10%and 15%deviation for transport mass and mass flow rate discharge,respectively,between predicted and actual values.Overall,this research provides insights into the critical factors influencing bucket elevator operation and offers a systematic methodology for optimizing bucket parameters,contributing to more efficient material handling in various industrial applications.
基金The National Natural Science Foundation of China(No.50805023)the Science and Technology Support Program of Jiangsu Province(No.BE2008081)+1 种基金the Transformation Program of Science and Technology Achievements of Jiangsu Province(No.BA2010093)the Program for Special Talent in Six Fields of Jiangsu Province(No.2008144)
文摘To segment defects from the quad flat non-lead QFN package surface a multilevel Otsu thresholding method based on the firefly algorithm with opposition-learning is proposed. First the Otsu thresholding algorithm is expanded to a multilevel Otsu thresholding algorithm. Secondly a firefly algorithm with opposition-learning OFA is proposed.In the OFA opposite fireflies are generated to increase the diversity of the fireflies and improve the global search ability. Thirdly the OFA is applied to searching multilevel thresholds for image segmentation. Finally the proposed method is implemented to segment the QFN images with defects and the results are compared with three methods i.e. the exhaustive search method the multilevel Otsu thresholding method based on particle swarm optimization and the multilevel Otsu thresholding method based on the firefly algorithm. Experimental results show that the proposed method can segment QFN surface defects images more efficiently and at a greater speed than that of the other three methods.
文摘Diaphanes is the fourth largest genus in Lampyridae, but no luciferase gene from this genus has been reported. In this paper, by PCR amplification of the genomic DNA, the luciferase gene of Diaphanes pectinealis, which is the first case from Diaphanes, was identified and sequenced. The luciferase gene from D. pectinealis spans 1958 base pairs （bp） from the start to the stop codon, including seven exons separated by six introns, and encoding a 547-residuelong polypeptide. Its deduced amino acid sequence showed high protein similarity to those of the Lampyrini tribe （93 - 94% ） and the Cratomorphini tribe （92%）, while low similarity was found with the North American firefly Photinus pyralis （83%） of the Photinini tribe within the same subfamily Lampyrinae. The phylogenetic analysis performed with the deduced amino acid sequences of the luciferase gene further confirms that D. pectinealis, Pyrocoelia, Lampyris, Cratomorphus, and Photinus belong to the same subfamily Lampyrinae, and Diaphanes is closely related to Pyrocoelia, Lampyris, and Cratomorphus. Furthemore, the phylogenetic analysis based on the nucleotide sequences of the luciferase gene indicates Diaphanes is a sister to Lampyris. The phylogenetic analyses are partly consistent with morphological （Branham ＆ Wenzel, 2003） and mitochondrial DNA analyses （Li et al, 2006）.
基金supported by the National Basic Research Program of China(No.2013CB228602)the National Science and Technology Major Project of China(No.2011ZX05004-003)the National High Technology Research Program of China(No.2013AA064202)
文摘Rayleigh waves have high amplitude, low frequency, and low velocity, which are treated as strong noise to be attenuated in reflected seismic surveys. This study addresses how to identify useful shear wave velocity profile and stratigraphic information from Rayleigh waves. We choose the Firefly algorithm for inversion of surface waves. The Firefly algorithm, a new type of particle swarm optimization, has the advantages of being robust, highly effective, and allows global searching. This algorithm is feasible and has advantages for use in Rayleigh wave inversion with both synthetic models and field data. The results show that the Firefly algorithm, which is a robust and practical method, can achieve nonlinear inversion of surface waves with high resolution.
基金the Natural Foundation of Sciences of Yunnan Province (2006C0046Q)Partly by the Chinese Academy of Sciences (O706551141)
文摘The cDNA encoding the luciferase from lantern mRNA of one diurnal firefly Pyrocoelia pygidialis Pic, 1926 has been cloned, sequenced and functionally expressed. The cDNA sequence of P pygidialis luciferase is 1647 base pairs in length, coding a protein of 548 amino acid residues. Sequence analysis of the deduced amino acid sequence showed that this luciferase had 97.8% resemblance to luciferases from the fireflies Lampyris noctiluca, Lampyris turkestanicus and Nyctophila cf. caucasica. Phylogenetic analysis using deduced amino acid sequence showed that P pygidialis located at the base of Lampyris＋Nyctophila clade with robust support （BP=97%）; but did not show a monophyletic relationship with its congeneric species P pectoralis, P tufa and P miyako, all three are strong luminous and nocturnal species. The expression worked in recombinant Escherichia coli. Expression product had a 70kDa band and emitted yellow-green luminescence in the presence of luciferin. Five loops in the P pygidialis luciferase, L1 （NI98-G208）, L2 （T240-G247）, L3 （G317-K322）, L4 （L343-I350） and L5 （G522-D532）, were found from the structure modeling analysis in the cleft, where it was considered the active site for the substrate compound entering and binding. Different amino acid residues between the luciferases of P. pygidialis and the three other known strong luminous species can not explain the situation of weak or strong luminescence. Future study of these loops, residues or crystal structure analysis may be helpful in understanding the real differences between the luciferases between diurnal and nocturnal species.
文摘The Firefly Algorithm(FA)is a highly efficient population-based optimization technique developed by mimicking the flashing behavior of fireflies when mating.This article proposes a method based on Differential Evolution(DE)/current-to-best/1 for enhancing the FA's movement process.The proposed modification increases the global search ability and the convergence rates while maintaining a balance between exploration and exploitation by deploying the global best solution.However,employing the best solution can lead to premature algorithm convergence,but this study handles this issue using a loop adjacent to the algorithm's main loop.Additionally,the suggested algorithm’s sensitivity to the alpha parameter is reduced compared to the original FA.The GbFA surpasses both the original and five-version of enhanced FAs in finding the optimal solution to 30 CEC2014 real parameter benchmark problems with all selected alpha values.Additionally,the CEC 2017 benchmark functions and the eight engineering optimization challenges are also utilized to evaluate GbFA’s efficacy and robustness on real-world problems against several enhanced algorithms.In all cases,GbFA provides the optimal result compared to other methods.Note that the source code of the GbFA algorithm is publicly available at https://www.optim-app.com/projects/gbfa.
文摘Classifying the data into a meaningful group is one of the fundamental ways of understanding and learning the valuable information. High-quality clustering methods are necessary for the valuable and efficient analysis of the increasing data. The Firefly Algorithm (FA) is one of the bio-inspired algorithms and it is recently used to solve the clustering problems. In this paper, Hybrid F-Firefly algorithm is developed by combining the Fuzzy C-Means (FCM) with FA to improve the clustering accuracy with global optimum solution. The Hybrid F-Firefly algorithm is developed by incorporating FCM operator at the end of each iteration in FA algorithm. This proposed algorithm is designed to utilize the goodness of existing algorithm and to enhance the original FA algorithm by solving the shortcomings in the FCM algorithm like the trapping in local optima and sensitive to initial seed points. In this research work, the Hybrid F-Firefly algorithm is implemented and experimentally tested for various performance measures under six different benchmark datasets. From the experimental results, it is observed that the Hybrid F-Firefly algorithm significantly improves the intra-cluster distance when compared with the existing algorithms like K-means, FCM and FA algorithm.
文摘Autonomous mobile robot navigation is one of the most emerging areas of research by using swarm intelligence. Path planning and obstacle avoidance are most researched current topics like navigational challenges for mobile robot. The paper presents application and implementation of Firefly Algorithm(FA)for Mobile Robot Navigation(MRN) in uncertain environment. The uncertainty is defined over the changing environmental condition from static to dynamic. The attraction of one firefly towards the other firefly due to variation of their brightness is the key concept of the proposed study. The proposed controller efficiently explores the environment and improves the global search in less number of iterations and hence it can be easily implemented for real time obstacle avoidance especially for dynamic environment. It solves the challenges of navigation, minimizes the computational calculations, and avoids random moving of fireflies. The performance of proposed controller is better in terms of path optimality when compared to other intelligent navigational approaches.
文摘Essentially, it is significant to supply the consumer with reliable and sufficient power. Since, power quality is measured by the consistency in frequency and power flow between control areas. Thus, in a power system operation and control,automatic generation control(AGC) plays a crucial role. In this paper, multi-area(Five areas: area 1, area 2, area 3, area 4 and area 5) reheat thermal power systems are considered with proportional-integral-derivative(PID) controller as a supplementary controller. Each area in the investigated power system is equipped with appropriate governor unit, turbine with reheater unit, generator and speed regulator unit. The PID controller parameters are optimized by considering nature bio-inspired firefly algorithm(FFA). The experimental results demonstrated the comparison of the proposed system performance(FFA-PID)with optimized PID controller based genetic algorithm(GAPID) and particle swarm optimization(PSO) technique(PSOPID) for the same investigated power system. The results proved the efficiency of employing the integral time absolute error(ITAE) cost function with one percent step load perturbation(1 % SLP) in area 1. The proposed system based FFA achieved the least settling time compared to using the GA or the PSO algorithms, while, it attained good results with respect to the peak overshoot/undershoot. In addition, the FFA performance is improved with the increased number of iterations which outperformed the other optimization algorithms based controller.
文摘Wireless Sensor Networks(WSNs)comprises low power devices that are randomly distributed in a geographically isolated region.The energy consumption of nodes is an essential factor to be considered.Therefore,an improved energy management technique is designed in this investigation to reduce its consumption and to enhance the network’s lifetime.This can be attained by balancing energy clusters using a meta-heuristic Firefly algorithm model for network communication.This improved technique is based on the cluster head selection technique with measurement of the tour length of fireflies.Time Division Multiple Access(TDMA)scheduler is also improved with the characteristics/behavior of fireflies and also executed.At last,the development approach shows the progression of the network lifetime,the total number of selected Cluster Heads(CH),the energy consumed by nodes,and the number of packets transmitted.This approach is compared with Ad hoc On-Demand Distance Vector(AODV),Dynamic Source Routing(DSR)and Low Energy Adaptive Clustering Hierarchy(LEAH)protocols.Simulation is performed in MATLAB with the numerical outcomes showing the efficiency of the proposed approach.The energy consumption of sensor nodes is reduced by about 50%and increases the lifetime of nodes by 78%more than AODV,DSR and LEACH protocols.The parameters such as cluster formation,end to end delay,percentage of nodes alive and packet delivery ratio,are also evaluated...The anticipated method shows better trade-off in contrast to existing techniques.
文摘This paper introduces a new approach of firefly algorithm based on opposition-based learning （OBFA） to enhance the global search ability of the original algorithm. The new algorithm employs opposition based learning concept to generate initial population and also updating agents’ positions. The proposed OBFA is applied for minimization of the factor of safety and search for critical failure surface in slope stability analysis. The numerical experiments demonstrate the effectiveness and robustness of the new algorithm.
基金Supported by the National Natural Science Foundation of China(61763019,61364025)the Science and Technology Foundation of Jiangxi Province,China(GJJ161076)
文摘Firefly algorithm(FA)is a recently-proposed swarm intelligence technique.It has shown good performance in solving various optimization problems.According to the standard firefly algorithm and most of its variants,a firefly migrates to every other brighter firefly in each iteration.However,this method leads to defects of oscillations of positions,which hampers the convergence to the optimum.To address these problems and enhance the performance of FA,we propose a new firefly algorithm,which is called the Best Neighbor Firefly Algorithm(BNFA).It employs the best neighbor guided strategy,where each firefly is attracted to the best firefly among some randomly chosen neighbors,thus reducing the firefly oscillations in every attraction-induced migration stage,while increasing the probability of the guidance a new better direction.Moreover,it selects neighbors randomly to prevent the firefly form being trapped into a local optimum.Extensive experiments are conducted to find out the optimal parameter settings.To verify the performance of BNFA,13 classical benchmark functions are tested.Results show that BNFA outperforms the standard FA and other recently proposed modified FAs.
基金This work is supported by the National Natural Science Foundation of China under Grant 61772179the Hunan Provincial Natural Science Foundation of China under Grant 2019JJ40005+3 种基金the Science and Technology Plan Project of Hunan Province under Grant 2016TP1020the Double First-Class University Project of Hunan Province under Grant Xiangjiaotong[2018]469the Open Fund Project of Hunan Provincial Key Laboratory of Intelligent Information Processing and Application for Hengyang Normal University under Grant IIPA19K02the Science Foundation of Hengyang Normal University under Grant 19QD13.
文摘Given a connected undirected graph G whose edges are labeled,the minimumlabeling spanning tree(MLST)problemis to find a spanning tree of G with the smallest number of different labels.TheMLST is anNP-hard combinatorial optimization problem,which is widely applied in communication networks,multimodal transportation networks,and data compression.Some approximation algorithms and heuristics algorithms have been proposed for the problem.Firefly algorithm is a new meta-heuristic algorithm.Because of its simplicity and easy implementation,it has been successfully applied in various fields.However,the basic firefly algorithm is not suitable for discrete problems.To this end,a novel discrete firefly algorithm for the MLST problem is proposed in this paper.A binary operation method to update firefly positions and a local feasible handling method are introduced,which correct unfeasible solutions,eliminate redundant labels,and make the algorithm more suitable for discrete problems.Computational results show that the algorithm has good performance.The algorithm can be extended to solve other discrete optimization problems.
基金the National Natural Science Foundation of China(No.51109090)the Natural Fund of Fujian Province(No.2015J01214)+2 种基金the Key Project of Fujian Provincial Department of Science & Technology(No.2012H0030)the University’s Innovative Project of Xiamen Science & Technology Bureau(No.3502Z20123019)the Project of New Century Excellent Talents of Colleges and Universities of Fujian Province(No.JA12181)
文摘In this paper, a new algorithm which integrates the powerful firefly Mgorithm （FA） and the ant colony optimization （ACO） has been used in tracking control of ship steering for optimization of fractional-order proportional-integral-derivative （FOPID） controller gains. Particle swarm optimization （PSO） algorithm is also used to optimize FOPID controllers, and their performances are compared. It is found that FA optimized FOPID controller gives better performance than others. Sensitivity analysis has been carried out to see the robustness of optimum FOPID gains obtained at nominal conditions to wide changes in system parameters, and the optimum FOPID gains need not be reset for wide changes in system parameters.
基金supported by the State Grid Science and Technology Project “Research on Technology System and Applications Scenarios of Artificial Intelligence in Power System” (No. SGZJ0000KXJS1800435)Key Technology Project of State Grid Shanghai Municipal Electric Power Company “Research and demonstration of Shanghai power grid reliability analysis platform”Key Technology Project of China Electric Power Research Institute “Research on setting calculation technology of power grid phase protection based on Artificial Intelligence” (JB83-19-007)
文摘To achieve optimal configuration of switching devices in a power distribution system,this paper proposes a repulsive firefly algorithm-based optimal switching device placement method.In this method,the influence of territorial repulsion during firefly courtship is considered.The algorithm is practically applied to optimize the position and quantity of switching devices,while avoiding its convergence to the local optimal solution.The experimental simulation results have showed that the proposed repulsive firefly algorithm is feasible and effective,with satisfying global search capability and convergence speed,holding potential applications in setting value calculation of relay protection and distribution network automation control.
文摘Software development effort estimation is considered a fundamental task for software development life cycle as well as for managing project cost, time and quality. Therefore, accurate estimation is a substantial factor in projects success and reducing the risks. In recent years, software effort estimation has received a considerable amount of attention from researchers?and became a challenge for software industry. In the last two decades, many researchers and practitioners proposed statistical and machine learning-based models for software effort estimation. In this work, Firefly Algorithm is proposed as a metaheuristic optimization method for optimizing the parameters of three COCOMO-based models. These models include the basic COCOMO model and other two models proposed in the literature as extensions of the basic COCOMO model. The developed estimation models are evaluated using different evaluation metrics. Experimental results show high accuracy and significant error minimization of Firefly Algorithm over other metaheuristic optimization algorithms including Genetic Algorithms and Particle Swarm Optimization.
文摘Congestion in Wireless Sensor Network (WSN) is an issue of concern for several researchers in recent years. The key challenge is to develop an algorithmic rule which may realize the optimased route on the idea of parameters like residual energy, range of retransmissions and the distance between source and destination. The Firefly Algorithmic rule is implemented in this paper that relies on the attractiveness issue of the firefly insect to control congestion in WSN at transport layer. The results additionally show that the projected approach is best as compared to Congestion Detection and Avoidance (CODA) and Particle Swarm Optimization (PSO) on network lifetime and throughput of the network.
